Heuristical Solution for Scheduling Single Stage Parallel Machines Production of Calcium Silicate Masonry Units with Sequence-Dependent Changeover Times to Improve Energy Efficiency

Abstract:

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Determination of optimal production schedules is a complex combinatorial task and may be dependent on various objectives. Hence, many mathematical problem formulations and solving strategies have already been proposed each considering individually constrained applications in order to minimize non-value-adding times or other cost driving factors. Nevertheless, obtaining optimal solutions is still related to extensive computational resources and time efforts.As a result, heuristical approaches or combinations of heuristics and exact algorithms are of major importance when it comes to automatically creating optimal production schedules. Considering the manufacturing of calcium silicate masonry units (CS), this paper describes an advancement for the General Lot-Sizing Problem (GLSP) in order to allow sequence-dependent changeovers as well as multiple different machines and backlogging (GLSPPLB). For solving the GLSPPLB, a heuristical algorithm consisting of neighborhood search and threshold accepting techniques was implemented. To validate the results of the heuristic and compare required computational resources to accurate mathematical solvers, a test set of a realistic scenario has been used.The developed heuristic is able to create nearly optimal production schedules and thereby minimizing the trade off between energy demand regarding idle times of production machinery and stocks. It is transferable to every discrete single stage production with similar constraints and can be used as an input for further simulations to improve energy consumption.

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T. Donhauser et al., Valid Methodology for Using Discrete Event Simulation to Improve the Resource Consumption for the Manufacturing of Masonry Units, in: Procedia CIRP, Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems, volume 41, (2016).